In this study, we apply our method to the Alzheimer’s Disease Neuroimaging Initiative (ADNI) datas (http://adni.loni.usc.edu/). Launched in 2003, the original goal of ADNI was to define imaging biomarkers for use in clinical trials of AD. The current goal has been extended to discover more effective methods to detect AD earlier at its pre-dementia stage. Data quality control was carefully conducted in the ADNI projects to make sure all the data from different imaging centers have the same imaging quality ([1]}) (e.g., same imaging protocol, same scanner, and comparable signal-to-noise ratio). The 7-min rs-fMRI data (140 volumes) was preprocessed using AFNI ([2]}) according to a standard pipeline ([3]}). Specifically, the first ten volumes are discarded, followed by a rigid-body head motion correction and a nonlinear spatial registration to the Montreal Neurological Institutes (MNI) space. Frame-wise displacement (FD \(> 0.5\) ) was considered as excessive head motion and the subjects with more than 2.5-min data (50 volumes) labeled as excessive head motion were discarded ([4]}).
FC was assessed using a functional brain atlas ([5]}) consisting of 268 nodes covering the entire brain. Mean rs-fMRI time series of each brain region was band-pass filtered (0.015–0.15 Hz) and further processed to reduce artifacts by regression analysis (nuisance regressors include head motion parameters (the “Friston-24” model), the mean BOLD signal of the white matter, and that of the cerebrospinal fluid).
